基于机器学习的HL-2A设备边界特征量多输出预测

IF 1.9 4区 工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY
Zelong Li, Peng Yu, Qianhong Huang, Qi Zeng, Qingyi Tan, Yijun Zhong, Zhe Wang, Haoran Ye, Zhanhui Wang, Wulv Zhong, Min Xu
{"title":"基于机器学习的HL-2A设备边界特征量多输出预测","authors":"Zelong Li,&nbsp;Peng Yu,&nbsp;Qianhong Huang,&nbsp;Qi Zeng,&nbsp;Qingyi Tan,&nbsp;Yijun Zhong,&nbsp;Zhe Wang,&nbsp;Haoran Ye,&nbsp;Zhanhui Wang,&nbsp;Wulv Zhong,&nbsp;Min Xu","doi":"10.1007/s10894-025-00499-y","DOIUrl":null,"url":null,"abstract":"<div><p>The study of heat flux and particle transport in the plasma boundary and divertor region is a key issue for the long-term stable operation of the fusion reactor in the future. SOLPS-ITER is one of the most widely used boundary simulation programs, however, its calculation cost is high, and the calculation time is long. To enable the effective and rapid prediction of characteristic quantities in the DSOL region and meet the physical coupling requirements between the boundary and core regions (DSOL region and plasma core), integrated simulation for fast core-edge coupling is necessary. By using the SOLPS-ITER code and combining the parameters of the HL-2A device, the influence of impurity injection on the physical characteristics of the divertor boundary is studied, and the relevant simulation data are obtained. Two reliable prediction models of plasma boundary feature quantities are constructed, which are fully connected neural network model (DSOL-NN) and convolutional neural network model (DSOL-CNN). In order to better meet the needs of fast integrated simulation of plasma core-edge coupling, a multi-input multi-output mode (MIMO) is adopted. The model considers the effects of different impurity species and injection rates on the electron temperature and particle flux density of the divertor target plate. The results show that both models can successfully predict the electron temperature of the divertor target plate, the particle flux density of the target plate and the core-edge <b><i>Z</i></b><sub><b><i>eff</i></b></sub> under different impurity injection rate conditions. In comparison, the convolutional neural network model in the two models shows better prediction performance, with a mean relative error of about 5%, which is less than 10% of the fully connected neural network. A large number of comparative predictions show that the neural network prediction model takes several orders of magnitude less than the SOLPS-ITER simulation time consuming, thus providing a basis for the rapid integrated simulation of core-edge coupling.</p></div>","PeriodicalId":634,"journal":{"name":"Journal of Fusion Energy","volume":"44 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-Output Prediction of HL-2A Device Boundary Characteristic Quantities Based on Machine Learning\",\"authors\":\"Zelong Li,&nbsp;Peng Yu,&nbsp;Qianhong Huang,&nbsp;Qi Zeng,&nbsp;Qingyi Tan,&nbsp;Yijun Zhong,&nbsp;Zhe Wang,&nbsp;Haoran Ye,&nbsp;Zhanhui Wang,&nbsp;Wulv Zhong,&nbsp;Min Xu\",\"doi\":\"10.1007/s10894-025-00499-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The study of heat flux and particle transport in the plasma boundary and divertor region is a key issue for the long-term stable operation of the fusion reactor in the future. SOLPS-ITER is one of the most widely used boundary simulation programs, however, its calculation cost is high, and the calculation time is long. To enable the effective and rapid prediction of characteristic quantities in the DSOL region and meet the physical coupling requirements between the boundary and core regions (DSOL region and plasma core), integrated simulation for fast core-edge coupling is necessary. By using the SOLPS-ITER code and combining the parameters of the HL-2A device, the influence of impurity injection on the physical characteristics of the divertor boundary is studied, and the relevant simulation data are obtained. Two reliable prediction models of plasma boundary feature quantities are constructed, which are fully connected neural network model (DSOL-NN) and convolutional neural network model (DSOL-CNN). In order to better meet the needs of fast integrated simulation of plasma core-edge coupling, a multi-input multi-output mode (MIMO) is adopted. The model considers the effects of different impurity species and injection rates on the electron temperature and particle flux density of the divertor target plate. The results show that both models can successfully predict the electron temperature of the divertor target plate, the particle flux density of the target plate and the core-edge <b><i>Z</i></b><sub><b><i>eff</i></b></sub> under different impurity injection rate conditions. In comparison, the convolutional neural network model in the two models shows better prediction performance, with a mean relative error of about 5%, which is less than 10% of the fully connected neural network. A large number of comparative predictions show that the neural network prediction model takes several orders of magnitude less than the SOLPS-ITER simulation time consuming, thus providing a basis for the rapid integrated simulation of core-edge coupling.</p></div>\",\"PeriodicalId\":634,\"journal\":{\"name\":\"Journal of Fusion Energy\",\"volume\":\"44 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Fusion Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10894-025-00499-y\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NUCLEAR SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Fusion Energy","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10894-025-00499-y","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUCLEAR SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

等离子体边界和导流区的热通量和粒子输运研究是未来核聚变反应堆长期稳定运行的关键问题。SOLPS-ITER是目前应用最广泛的边界模拟程序之一,但其计算成本高,计算时间长。为了实现对DSOL区域特征量的有效、快速预测,满足边界与核心区(DSOL区域与等离子体核)之间的物理耦合要求,需要对核心-边缘快速耦合进行集成仿真。利用SOLPS-ITER代码,结合HL-2A装置的参数,研究了杂质注入对导流器边界物理特性的影响,并获得了相关的仿真数据。建立了两种可靠的等离子体边界特征量预测模型,即全连接神经网络模型(DSOL-NN)和卷积神经网络模型(DSOL-CNN)。为了更好地满足等离子体核心-边缘耦合快速集成仿真的需要,采用了多输入多输出模式(MIMO)。该模型考虑了不同杂质种类和注入速率对导流器靶板电子温度和粒子通量密度的影响。结果表明,两种模型均能较好地预测不同杂质注入速率条件下的导流器靶板的电子温度、靶板的粒子通量密度和芯边Zeff。相比之下,两种模型中的卷积神经网络模型表现出更好的预测性能,平均相对误差在5%左右,小于全连接神经网络的10%。大量对比预测表明,神经网络预测模型比SOLPS-ITER模拟耗时少几个数量级,为快速集成模拟核边耦合提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Output Prediction of HL-2A Device Boundary Characteristic Quantities Based on Machine Learning

The study of heat flux and particle transport in the plasma boundary and divertor region is a key issue for the long-term stable operation of the fusion reactor in the future. SOLPS-ITER is one of the most widely used boundary simulation programs, however, its calculation cost is high, and the calculation time is long. To enable the effective and rapid prediction of characteristic quantities in the DSOL region and meet the physical coupling requirements between the boundary and core regions (DSOL region and plasma core), integrated simulation for fast core-edge coupling is necessary. By using the SOLPS-ITER code and combining the parameters of the HL-2A device, the influence of impurity injection on the physical characteristics of the divertor boundary is studied, and the relevant simulation data are obtained. Two reliable prediction models of plasma boundary feature quantities are constructed, which are fully connected neural network model (DSOL-NN) and convolutional neural network model (DSOL-CNN). In order to better meet the needs of fast integrated simulation of plasma core-edge coupling, a multi-input multi-output mode (MIMO) is adopted. The model considers the effects of different impurity species and injection rates on the electron temperature and particle flux density of the divertor target plate. The results show that both models can successfully predict the electron temperature of the divertor target plate, the particle flux density of the target plate and the core-edge Zeff under different impurity injection rate conditions. In comparison, the convolutional neural network model in the two models shows better prediction performance, with a mean relative error of about 5%, which is less than 10% of the fully connected neural network. A large number of comparative predictions show that the neural network prediction model takes several orders of magnitude less than the SOLPS-ITER simulation time consuming, thus providing a basis for the rapid integrated simulation of core-edge coupling.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Fusion Energy
Journal of Fusion Energy 工程技术-核科学技术
CiteScore
2.20
自引率
0.00%
发文量
24
审稿时长
2.3 months
期刊介绍: The Journal of Fusion Energy features original research contributions and review papers examining and the development and enhancing the knowledge base of thermonuclear fusion as a potential power source. It is designed to serve as a journal of record for the publication of original research results in fundamental and applied physics, applied science and technological development. The journal publishes qualified papers based on peer reviews. This journal also provides a forum for discussing broader policies and strategies that have played, and will continue to play, a crucial role in fusion programs. In keeping with this theme, readers will find articles covering an array of important matters concerning strategy and program direction.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信